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METABOLIC conditions, particularly coronary heart disease (CHD) and Type 2 diabetes, are strongly associated with specific fat distribution patterns. This is according to the results of a body composition analysis using a technique created by AMRA, Linköping, Sweden, which could aid the development of new treatments for these conditions.
Body Composition Profiling
Metabolic syndrome, a cluster of conditions including obesity and Type 2 diabetes, increases the risk of developing a number of chronic diseases and is present in around a quarter of adults across the globe. To try and better understand the link between fat distribution and specific metabolic conditions, body composition profiling was undertaken on 6,000 subjects from the UK Biobank.
Fat Distribution Patterns
This revealed skewed fat distribution patterns associated with different metabolic disease profiles regardless of whether the subject’s BMI was categorised as normal, overweight, or obese. While some of these phenotypes exhibited no metabolic disease, others were linked with CHD, Type 2 diabetes, or the comorbidity of the two. For example, both CHD and Type 2 diabetes were associated with higher visceral and muscle fat, Type 2 diabetes with higher liver fat, and CHD with lower liver fat. Additionally, metabolic health was linked to lower visceral and muscle fat, while liver fat was non-significant in this regard. The results remained significant after adjusting for sex, age, BMI, alcohol, smoking, and physical activity.
Better Understanding
The study shows that researchers need to look beyond factors such as sex, age, and lifestyle to better understand metabolic diseases and develop new treatments. In particular, measuring and investigating numerous fat compartments to find fat distribution patterns appears to be important to advance the ways these conditions are managed and prevented in the future.
Multivariate Approach
Senior author of the study, Dr Olof Dahlqvist Leinhard, Linköping University, commented: “It has been known for some time that there are fat distributions that are disadvantageous from a health perspective. Today, new techniques provide high accuracy and precision, enabling in-depth analyses of the clinical importance of body composition at a large scale. What’s exciting is that, by using a multivariate approach and an intuitive visualisation of body composition, we’ve been able to identify a wide range of body composition profiles that could provide the link to increased risk of metabolic diseases.”
James Coker, Reporter
